BHB identification in LAMOST DR10 with deep learning

With massive spectral data provided by the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST), we aim to identify more potential BHB stars using machine learning methods. In this study, we propose BHBNet, an advanced two-stage deep learning model integrating multiple techniques.

By implementing Bayesian inference, it not only provides classification results but also quantifies uncertainty. In stage 1, a six-class classification model was constructed to initially identify BHB candidates, achieving a precision of 95.43% on the test set. In stage 2, a binary classification model constructed through the transfer learning method was employed to further refine the candidates, achieving a precision of 98.36% on the test set.